AIMC Topic: Semantics

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A pre-trained BERT for Korean medical natural language processing.

Scientific reports
With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language m...

SFN: A Novel Scalable Feature Network for Vulnerability Representation of Open-Source Codes.

Computational intelligence and neuroscience
Vulnerability detection technology has become a hotspot in the field of software security, and most of the current methods do not have a complete consideration during code characterizing, which leads to problems such as information loss. Therefore, t...

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language.

Frontiers in public health
Handwritten prescriptions and radiological reports: doctors use handwritten prescriptions and radiological reports to give drugs to patients who have illnesses, injuries, or other problems. Clinical text data, like physician prescription visuals and ...

Hierarchical and Self-Attended Sequence Autoencoder.

IEEE transactions on pattern analysis and machine intelligence
It is important and challenging to infer stochastic latent semantics for natural language applications. The difficulty in stochastic sequential learning is caused by the posterior collapse in variational inference. The input sequence is disregarded i...

A hierarchy of linguistic predictions during natural language comprehension.

Proceedings of the National Academy of Sciences of the United States of America
Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However,...

A Plug-in Method for Representation Factorization in Connectionist Models.

IEEE transactions on neural networks and learning systems
In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically controllable factors in a semisupervised manner, without modifying the origin...

Weighted Joint Sentiment-Topic Model for Sentiment Analysis Compared to ALGA: Adaptive Lexicon Learning Using Genetic Algorithm.

Computational intelligence and neuroscience
Latent Dirichlet Allocation (LDA) is an approach to unsupervised learning that aims to investigate the semantics among words in a document as well as the influence of a subject on a word. As an LDA-based model, Joint Sentiment-Topic (JST) examines th...

An Effective Approach of Vehicle Detection Using Deep Learning.

Computational intelligence and neuroscience
With the rise of unmanned driving and intelligent transportation research, great progress has been made in vehicle detection technology. The purpose of this paper is employing the method of deep learning to study the vehicle detection algorithm, in w...

Multidimensional Latent Semantic Networks for Text Humor Recognition.

Sensors (Basel, Switzerland)
Humor is a special human expression style, an important "lubricant" for daily communication for people; people can convey emotional messages that are not easily expressed through humor. At present, artificial intelligence is one of the popular resear...

Color Design Decisions for Ceramic Products Based on Quantification of Perceptual Characteristics.

Sensors (Basel, Switzerland)
The appearance characteristics of ceramic color are an important factor in determining the user's aesthetic perception of the product. Given the problem that ceramic color varies and the user's visual sensory evaluation of color is highly subjective ...